fac2x2analyze {factorial2x2} | R Documentation |
Performs significance testing for the Proportional Allocation 2, Equal Allocation 3, Equal Allocation 2 procedures. Also reports the hazard ratios, 95% confidence intervals, p-values, nominal significance levels, and correlations for the overall and simple test statistics.
fac2x2analyze(time, event, indA, indB, covmat, alpha, dig = 2, niter = 5)
time |
follow-up times |
event |
event indicators (0/1) |
indA |
treatment A indicators (0/1) |
indB |
treatment B indicators (0/1) |
covmat |
covariate matrix, must be non-NULL. Factor variables MUST use 0/1 dummy variables |
alpha |
two-sided familywise significance level |
dig |
number of decimal places to which we |
niter |
number of interations passed to |
For each of the three multiple testing procedures, the critical values for the overall A (respectively, simple A) logrank statistics may be slightly different from the overall B (respectively, simple B) logrank statistics. This is due to their slightly different correlations with each other (i.e., correlation between overall A and simple A, respectively, overall B and simple B, statistics) as well as with the simple AB statistic.
loghrAoverall |
overall A log hazard ratio |
seAoverall |
standard error of the overall A log hazard ratio |
ZstatAoverall |
Z-statistic for the overall A log hazard ratio |
pvalAoverall |
two-sided p-value for the overall hazard ratio |
hrAoverall |
overall A hazard ratio |
ciAoverall |
95% confidence interval for the overall A hazard ratio |
loghrAsimple |
simple A log hazard ratio |
seAsimple |
standard error of the simple A log hazard ratio |
ZstatAsimple |
Z-statistic for the simple A log hazard ratio |
pvalAsimple |
two-sided p-value for the simple A hazard ratio |
hrAsimple |
simple A hazard ratio |
ciAsimple |
95% confidence interval for the simple A hazard ratio |
loghrABsimple |
simple AB log hazard ratio |
seABsimple |
standard error of the simple AB log hazard ratio |
ZstatABsimple |
Z-statistic for the simple AB log hazard ratio |
pvalABsimple |
two-sided p-value for the simple AB hazard ratio |
hrABsimple |
simple AB hazard ratio |
ciABsimple |
95% confidence interval for the simple AB hazard ratio |
critEA3_A |
Equal Allocation 3's critical value for the overall A simple A, and simple AB hypotheses |
sigEA3_A |
Equal Allocation 3's p-value rejection criterion for the overall A, simple A, and simple AB hypotheses |
resultEA3_A |
Equal Allocation 3's accept/reject decisions for the overall A, simple A, and simple AB hypotheses |
critPA2overallA |
Proportional Allocation 2's critical value for the overall A statistic |
sigPA2overallA |
Proportional Allocation 2's p-value rejection criterion for the overall A hypothesis |
critPA2simpleAB |
Proportional Allocation 2's critical value for the simple AB hypothesis |
sigPA2simpleAB |
Proportional Allocation 2 procedure's p-value rejection criterion for the simple AB hypothesis |
resultPA2_A |
Proportional Allocation 2 procedure's accept/reject decisions for the overall A and simple A hypotheses |
critEA2_A |
Equal Allocation 2 procedure's critical value for the simple A and simple AB hypotheses |
sigEA2_A |
Equal Allocation 2 procedure's p-value rejection criterion for the simple A and simple AB hypotheses |
resultEA2_A |
Equal Allocation 2 procedure's accept/reject decisions for the simple A and simple AB hypotheses |
corAa |
correlation between the overall A and simple A logrank statistics |
corAab |
correlation between the overall A and simple AB logrank statistics |
coraab |
correlation between the simple A and simple AB logrank statistics |
Eric Leifer, James Troendle
Leifer, E.S., Troendle, J.F., Kolecki, A., Follmann, D. Joint testing of overall and simple effect for the two-by-two factorial design. (2020). Submitted.
Lin, D-Y., Gong, J., Gallo, P., et al. Simultaneous inference on treatment effects in survival studies with factorial designs. Biometrics. 2016; 72: 1078-1085.
Slud, E.V. Analysis of factorial survival experiments. Biometrics. 1994; 50: 25-38.
# First load the simulated data variables. The "simdataSub" file is # a 100-by-9 matrix which is loaded with the factorial2x2 package. time <- simdataSub[, "time"] event <- simdataSub[, "event"] indA <- simdataSub[, "indA"] indB <- simdataSub[, "indB"] covmat <- simdataSub[, 6:10] fac2x2analyze(time, event, indA, indB, covmat, alpha = 0.05, niter = 5) # $loghrA # [1] 0.05613844 # $seA # [1] 0.4531521 # $ZstatA # [1] 0.1238843 # $pvalA # [1] 0.9014069 # $hrA # [1] 1.057744 # $ciA # [1] 0.4351608 2.5710556 # $loghra # [1] 0.1987329 # $sea # [1] 0.6805458 # $Zstata # [1] 0.2920198 # $pvala # [1] 0.7702714 # $hra # [1] 1.219856 # $cia # [1] 0.3213781 4.6302116 # $loghrab # [1] 0.2864932 # $seab # [1] 0.6762458 # $Zstatab # [1] 0.4236525 # $pvalab # [1] 0.6718193 # $hrab # [1] 1.331749 # $ciab # [1] 0.3538265 5.0125010 # $critPA2A # [1] -2.129 # $sigPA2A # [1] 0.03325426 # $critPA2ab # [1] -2.299 # $sigPA2ab # [1] 0.02150494 # $result23 # [1] "accept overall A" "accept simple AB" # $critEA3 # [1] -2.338 # $sigEA3 # [1] 0.01938725 # $result13 # [1] "accept overall A" "accept simple A" "accept simple AB" # $critEA2 # [1] -2.216 # $sigEA2 # [1] 0.0266915 # $result12 # [1] "accept simple A" "accept simple AB" # $corAa # [1] 0.6123399 # $corAab # [1] 0.5675396 # $coraab # [1] 0.4642737